require(igraph)
g <- make_graph("Zachary")
plot(g)
R Sienna Tutorial
Data
getwd()
load("twitter_20190919.RData") #change to your working directory
str(twitter_20190919, 1)
keyf <- twitter_20190919[[1]]
#keyf is dataframe on 147 Dutch MPs
mydata <- twitter_20190919[[2]]
# mydata is object ready to analyze in RSiena. Nodes are the same as in keyf and seats. Contains twitter data at three timepounts. Three layers: fnet (who follows whom), atmnet (who amentions whom) and rnter (who retweets whom). Also contains timevariant information on nodes.
seats <- twitter_20190919[[3]]
#seats is dataset which contains coordinates of seats in House of Parliament in Netherlands
Densities
# retrieve nominationdata from rsiena object
fnet <- mydata$depvars$fnet
atmnet <- mydata$depvars$atmnet
rtnet <- mydata$depvars$rtnet
# retrieve node-attributes from rsiena object
vrouw <- mydata$cCovars$vrouw
partij <- mydata$cCovars$partij
ethminz <- mydata$cCovars$ethminz
lft <- mydata$cCovars$lft
# de-mean-center node attributes
ethminz <- ethminz + attributes(ethminz)$mean
partij <- partij + attributes(partij)$mean
vrouw <- vrouw + attributes(vrouw)$mean
lft <- lft + attributes(lft)$mean
# construct matrices for similarity for each dimension (dyad characteristics)
vrouwm <- fhomomat(vrouw)
partijm <- fhomomat(partij)
ethminzm <- fhomomat(ethminz)
# just for fun, make dyad characteristic indicating whether both nodes are ethnic minorities
xmat <- matrix(ethminz, nrow = length(ethminz), ncol = length(ethminz))
xmatt <- t(xmat)
minoritym <- xmat == 1 & xmatt == 1
# for age max 5 year difference / for descriptives
xmat <- matrix(lft, nrow = length(lft), ncol = length(lft))
xmatt <- t(xmat)
lftm <- (abs(xmat - xmatt) < 6)
# calculate all possible similar dyads, not the focus of this exercise. fndyads2(fnet[,,1], vrouwm)
# fndyads2(fnet[,,3], vrouwm) fndyads2(fnet[,,1], partijm) fndyads2(fnet[,,3], partijm)
# fndyads2(fnet[,,1], ethminzm) fndyads2(fnet[,,3], ethminzm)
# make a big object to store all results
desmat <- matrix(NA, nrow = 10, ncol = 9)
# lets start using our functions
desmat[1, 1] <- fdensity(fnet[, , 1])
desmat[1, 2] <- fdensity(fnet[, , 2])
desmat[1, 3] <- fdensity(fnet[, , 3])
desmat[2, 1] <- fdensityintra(fnet[, , 1], vrouwm)
desmat[2, 2] <- fdensityintra(fnet[, , 2], vrouwm)
desmat[2, 3] <- fdensityintra(fnet[, , 3], vrouwm)
desmat[3, 1] <- fdensityinter(fnet[, , 1], vrouwm)
desmat[3, 2] <- fdensityinter(fnet[, , 2], vrouwm)
desmat[3, 3] <- fdensityinter(fnet[, , 3], vrouwm)
desmat[4, 1] <- fdensityintra(fnet[, , 1], partijm)
desmat[4, 2] <- fdensityintra(fnet[, , 2], partijm)
desmat[4, 3] <- fdensityintra(fnet[, , 3], partijm)
desmat[5, 1] <- fdensityinter(fnet[, , 1], partijm)
desmat[5, 2] <- fdensityinter(fnet[, , 2], partijm)
desmat[5, 3] <- fdensityinter(fnet[, , 3], partijm)
desmat[6, 1] <- fdensityintra(fnet[, , 1], ethminzm)
desmat[6, 2] <- fdensityintra(fnet[, , 2], ethminzm)
desmat[6, 3] <- fdensityintra(fnet[, , 3], ethminzm)
desmat[7, 1] <- fdensityinter(fnet[, , 1], ethminzm)
desmat[7, 2] <- fdensityinter(fnet[, , 2], ethminzm)
desmat[7, 3] <- fdensityinter(fnet[, , 3], ethminzm)
desmat[8, 1] <- fdensityinter(fnet[, , 1], minoritym)
desmat[8, 2] <- fdensityinter(fnet[, , 2], minoritym)
desmat[8, 3] <- fdensityinter(fnet[, , 3], minoritym)
desmat[9, 1] <- fdensityintra(fnet[, , 1], lftm)
desmat[9, 2] <- fdensityintra(fnet[, , 2], lftm)
desmat[9, 3] <- fdensityintra(fnet[, , 3], lftm)
desmat[10, 1] <- fdensityinter(fnet[, , 1], lftm)
desmat[10, 2] <- fdensityinter(fnet[, , 2], lftm)
desmat[10, 3] <- fdensityinter(fnet[, , 3], lftm)
desmat[1, 1 + 3] <- fdensity(atmnet[, , 1])
desmat[1, 2 + 3] <- fdensity(atmnet[, , 2])
desmat[1, 3 + 3] <- fdensity(atmnet[, , 3])
desmat[2, 1 + 3] <- fdensityintra(atmnet[, , 1], vrouwm)
desmat[2, 2 + 3] <- fdensityintra(atmnet[, , 2], vrouwm)
desmat[2, 3 + 3] <- fdensityintra(atmnet[, , 3], vrouwm)
desmat[3, 1 + 3] <- fdensityinter(atmnet[, , 1], vrouwm)
desmat[3, 2 + 3] <- fdensityinter(atmnet[, , 2], vrouwm)
desmat[3, 3 + 3] <- fdensityinter(atmnet[, , 3], vrouwm)
desmat[4, 1 + 3] <- fdensityintra(atmnet[, , 1], partijm)
desmat[4, 2 + 3] <- fdensityintra(atmnet[, , 2], partijm)
desmat[4, 3 + 3] <- fdensityintra(atmnet[, , 3], partijm)
desmat[5, 1 + 3] <- fdensityinter(atmnet[, , 1], partijm)
desmat[5, 2 + 3] <- fdensityinter(atmnet[, , 2], partijm)
desmat[5, 3 + 3] <- fdensityinter(atmnet[, , 3], partijm)
desmat[6, 1 + 3] <- fdensityintra(atmnet[, , 1], ethminzm)
desmat[6, 2 + 3] <- fdensityintra(atmnet[, , 2], ethminzm)
desmat[6, 3 + 3] <- fdensityintra(atmnet[, , 3], ethminzm)
desmat[7, 1 + 3] <- fdensityinter(atmnet[, , 1], ethminzm)
desmat[7, 2 + 3] <- fdensityinter(atmnet[, , 2], ethminzm)
desmat[7, 3 + 3] <- fdensityinter(atmnet[, , 3], ethminzm)
desmat[8, 1 + 3] <- fdensityinter(atmnet[, , 1], minoritym)
desmat[8, 2 + 3] <- fdensityinter(atmnet[, , 2], minoritym)
desmat[8, 3 + 3] <- fdensityinter(atmnet[, , 3], minoritym)
desmat[9, 1 + 3] <- fdensityintra(atmnet[, , 1], lftm)
desmat[9, 2 + 3] <- fdensityintra(atmnet[, , 2], lftm)
desmat[9, 3 + 3] <- fdensityintra(atmnet[, , 3], lftm)
desmat[10, 1 + 3] <- fdensityinter(atmnet[, , 1], lftm)
desmat[10, 2 + 3] <- fdensityinter(atmnet[, , 2], lftm)
desmat[10, 3 + 3] <- fdensityinter(atmnet[, , 3], lftm)
desmat[1, 1 + 6] <- fdensity(rtnet[, , 1])
desmat[1, 2 + 6] <- fdensity(rtnet[, , 2])
desmat[1, 3 + 6] <- fdensity(rtnet[, , 3])
desmat[2, 1 + 6] <- fdensityintra(rtnet[, , 1], vrouwm)
desmat[2, 2 + 6] <- fdensityintra(rtnet[, , 2], vrouwm)
desmat[2, 3 + 6] <- fdensityintra(rtnet[, , 3], vrouwm)
desmat[3, 1 + 6] <- fdensityinter(rtnet[, , 1], vrouwm)
desmat[3, 2 + 6] <- fdensityinter(rtnet[, , 2], vrouwm)
desmat[3, 3 + 6] <- fdensityinter(rtnet[, , 3], vrouwm)
desmat[4, 1 + 6] <- fdensityintra(rtnet[, , 1], partijm)
desmat[4, 2 + 6] <- fdensityintra(rtnet[, , 2], partijm)
desmat[4, 3 + 6] <- fdensityintra(rtnet[, , 3], partijm)
desmat[5, 1 + 6] <- fdensityinter(rtnet[, , 1], partijm)
desmat[5, 2 + 6] <- fdensityinter(rtnet[, , 2], partijm)
desmat[5, 3 + 6] <- fdensityinter(rtnet[, , 3], partijm)
desmat[6, 1 + 6] <- fdensityintra(rtnet[, , 1], ethminzm)
desmat[6, 2 + 6] <- fdensityintra(rtnet[, , 2], ethminzm)
desmat[6, 3 + 6] <- fdensityintra(rtnet[, , 3], ethminzm)
desmat[7, 1 + 6] <- fdensityinter(rtnet[, , 1], ethminzm)
desmat[7, 2 + 6] <- fdensityinter(rtnet[, , 2], ethminzm)
desmat[7, 3 + 6] <- fdensityinter(rtnet[, , 3], ethminzm)
desmat[8, 1 + 6] <- fdensityinter(rtnet[, , 1], minoritym)
desmat[8, 2 + 6] <- fdensityinter(rtnet[, , 2], minoritym)
desmat[8, 3 + 6] <- fdensityinter(rtnet[, , 3], minoritym)
desmat[9, 1 + 6] <- fdensityintra(rtnet[, , 1], lftm)
desmat[9, 2 + 6] <- fdensityintra(rtnet[, , 2], lftm)
desmat[9, 3 + 6] <- fdensityintra(rtnet[, , 3], lftm)
desmat[10, 1 + 6] <- fdensityinter(rtnet[, , 1], lftm)
desmat[10, 2 + 6] <- fdensityinter(rtnet[, , 2], lftm)
desmat[10, 3 + 6] <- fdensityinter(rtnet[, , 3], lftm)
colnames(desmat) <- c("friends w1", "friends w2", "friends w3", "atmentions w1", "atmentions w2", "atmentions w3",
"retweets w1", "retweets w2", "retweets w3")
rownames(desmat) <- c("total", "same sex", "different sex", "same party", "different party", "same ethnicity",
"different ethnicity", "both minority", "same age (<6)", "different age (>5)")
desmat
# we observe a lot of homophily. Mainly big difference in density between and whithin political parties. Homophily is not that strong across social dimensions.
coleman homophily
# Because size of different subgroups vary and number of out-degrees differs between MPs, whitin party densities might be higher when MPs randomly select partner/alter. Segregation will partly be structully induced by differences in relative groups sizes and activity on twitter. Coleman's homophily index: takes relative group sizes and differences into account. 0 -> observed number of within-group ties is the same as would be expected under random choice. 1 -> maximum segregation. -1 -> MPs maximally avoid within group relations.
colmat <- matrix(NA, nrow = 3, ncol = 9)
colmat[1, 1] <- fscolnet(fnet[, , 1], partij)
colmat[1, 2] <- fscolnet(fnet[, , 2], partij)
colmat[1, 3] <- fscolnet(fnet[, , 3], partij)
colmat[1, 4] <- fscolnet(atmnet[, , 1], partij)
colmat[1, 5] <- fscolnet(atmnet[, , 2], partij)
colmat[1, 6] <- fscolnet(atmnet[, , 3], partij)
colmat[1, 7] <- fscolnet(rtnet[, , 1], partij)
colmat[1, 8] <- fscolnet(rtnet[, , 2], partij)
colmat[1, 9] <- fscolnet(rtnet[, , 3], partij)
colmat[2, 1] <- fscolnet(fnet[, , 1], vrouw)
colmat[2, 2] <- fscolnet(fnet[, , 2], vrouw)
colmat[2, 3] <- fscolnet(fnet[, , 3], vrouw)
colmat[2, 4] <- fscolnet(atmnet[, , 1], vrouw)
colmat[2, 5] <- fscolnet(atmnet[, , 2], vrouw)
colmat[2, 6] <- fscolnet(atmnet[, , 3], vrouw)
colmat[2, 7] <- fscolnet(rtnet[, , 1], vrouw)
colmat[2, 8] <- fscolnet(rtnet[, , 2], vrouw)
colmat[2, 9] <- fscolnet(rtnet[, , 3], vrouw)
colmat[3, 1] <- fscolnet(fnet[, , 1], ethminz)
colmat[3, 2] <- fscolnet(fnet[, , 2], ethminz)
colmat[3, 3] <- fscolnet(fnet[, , 3], ethminz)
colmat[3, 4] <- fscolnet(atmnet[, , 1], ethminz)
colmat[3, 5] <- fscolnet(atmnet[, , 2], ethminz)
colmat[3, 6] <- fscolnet(atmnet[, , 3], ethminz)
colmat[3, 7] <- fscolnet(rtnet[, , 1], ethminz)
colmat[3, 8] <- fscolnet(rtnet[, , 2], ethminz)
colmat[3, 9] <- fscolnet(rtnet[, , 3], ethminz)
colnames(colmat) <- c("friends w1", "friends w2", "friends w3", "atmentions w1", "atmentions w2", "atmentions w3",
"retweets w1", "retweets w2", "retweets w3")
rownames(colmat) <- c("party", "sex", "ethnicity")
colmat
RSienna
# defining myeff object
library(RSiena)
myeff <- getEffects(mydata)
myeff
myeff_m1 <- myeff
myeff_m1 <- includeEffects(myeff_m1, sameX, interaction1 = "partij", name = "rtnet")
# I used a seed so you will probably see the same results
myalgorithm <- sienaAlgorithmCreate(projname = "test", seed = 345654)
# to speed things up a bit, I am using more cores.
ansM1 <- siena07(myalgorithm, data = mydata, effects = myeff_m1, useCluster = TRUE, nbrNodes = 4, initC = TRUE,
batch = TRUE)
ansM1b <- siena07(myalgorithm, data = mydata, prevAns = ansM1, effects = myeff_m1, useCluster = TRUE,
nbrNodes = 4, initC = TRUE, batch = TRUE)
ansM1c <- siena07(myalgorithm, data = mydata, prevAns = ansM1b, effects = myeff_m1, useCluster = TRUE,
nbrNodes = 4, initC = TRUE, batch = TRUE)
save(ansM1, file = "ansM1a.RData")
save(ansM1b, file = "ansM1b.RData")
save(ansM1c, file = "ansM1c.RData")
load("ansM1a.RData")
load("ansM1b.RData")
load("ansM1c.RData")
ansM1
ansM1b
ansM1c
#To what extent do we observe segregation along party affiliation in the retweet network among Dutch MPs?
# Answer: the eval same partij is 1.8551. This means that we observe strong segregation along pary affiliation: people are more likely to have ties with people from the same party
RQ 1
myeff_m2 <- myeff
myeff_m2 <- includeEffects(myeff_m2, sameX, interaction1 = "partij", name = "rtnet")
myeff_m2 <- myeff
myeff_m2 <- includeEffects(myeff_m2, sameX, interaction1 = "vrouw", name = "rtnet")
myeff_m2 <- myeff
myeff_m2 <- includeEffects(myeff_m2, sameX, interaction1 = "lft", name = "rtnet")
myeff_m2 <- myeff
myeff_m2 <- includeEffects(myeff_m2, X, interaction1 = "afstand", name = "rtnet")
---
title: "R Notebook"
output: html_notebook
---



```{r}
require(igraph)
g <- make_graph("Zachary")
plot(g)

```

```{r}

```


```{r}

```




R Sienna Tutorial 

```{r}
# cleanup workspace
rm(list = ls())

# install packages
library(RSiena)
library(selenider)
library(rvest)
library(tidyverse)
library(netstat)
library(pingr)
library(jsonlite)
library(stringr)
library(openalexR)

# density: observed relations divided by possible relations
fdensity <- function(x) {
    # x is your nomination network make sure diagonal cells are NA
    diag(x) <- NA
    # take care of RSiena structural zeros, set as missing.
    x[x == 10] <- NA
    sum(x == 1, na.rm = T)/(sum(x == 1 | x == 0, na.rm = T))
}

# calculate intragroup density
fdensityintra <- function(x, A) {
    # A is matrix indicating whether nodes in dyad have same node attributes
    diag(x) <- NA
    x[x == 10] <- NA
    diag(A) <- NA
    sum(x == 1 & A == 1, na.rm = T)/(sum((x == 1 | x == 0) & A == 1, na.rm = T))
}

# calculate intragroup density
fdensityinter <- function(x, A) {
    # A is matrix indicating whether nodes in dyad have same node attributes
    diag(x) <- NA
    x[x == 10] <- NA
    diag(A) <- NA
    sum(x == 1 & A != 1, na.rm = T)/(sum((x == 1 | x == 0) & A != 1, na.rm = T))
}

# construct dyadcharacteristic whether nodes are similar/homogenous
fhomomat <- function(x) {
    # x is a vector of node-covariate
    xmat <- matrix(x, nrow = length(x), ncol = length(x))
    xmatt <- t(xmat)
    xhomo <- xmat == xmatt
    return(xhomo)
}

# a function to calculate all valid dyads.
fndyads <- function(x) {
    diag(x) <- NA
    x[x == 10] <- NA
    (sum((x == 1 | x == 0), na.rm = T))
}

# a function to calculate all valid intragroupdyads.
fndyads2 <- function(x, A) {
    diag(x) <- NA
    x[x == 10] <- NA
    diag(A) <- NA
    (sum((x == 1 | x == 0) & A == 1, na.rm = T))
}


fscolnet <- function(network, ccovar) {
    # Calculate coleman on network level:
    # https://reader.elsevier.com/reader/sd/pii/S0378873314000239?token=A42F99FF6E2B750436DD2CB0DB7B1F41BDEC16052A45683C02644DAF88215A3379636B2AA197B65941D6373E9E2EE413
    
    fhomomat <- function(x) {
        xmat <- matrix(x, nrow = length(x), ncol = length(x))
        xmatt <- t(xmat)
        xhomo <- xmat == xmatt
        return(xhomo)
    }
    
    fsumintra <- function(x, A) {
        # A is matrix indicating whether nodes constituting dyad have same characteristics
        diag(x) <- NA
        x[x == 10] <- NA
        diag(A) <- NA
        sum(x == 1 & A == 1, na.rm = T)
    }
    
    # expecation w*=sum_g sum_i (ni((ng-1)/(N-1)))
    network[network == 10] <- NA
    ni <- rowSums(network, na.rm = T)
    ng <- NA
    for (i in 1:length(ccovar)) {
        ng[i] <- table(ccovar)[rownames(table(ccovar)) == ccovar[i]]
    }
    N <- length(ccovar)
    wexp <- sum(ni * ((ng - 1)/(N - 1)), na.rm = T)
    
    # wgg1 how many intragroup ties
    w <- fsumintra(network, fhomomat(ccovar))
    
    Scol_net <- ifelse(w >= wexp, (w - wexp)/(sum(ni, na.rm = T) - wexp), (w - wexp)/wexp)
    return(Scol_net)
}

```


Data
```{r}
getwd()

load("twitter_20190919.RData")  #change to your working directory
str(twitter_20190919, 1)

keyf <- twitter_20190919[[1]]
#keyf is dataframe on 147 Dutch MPs
mydata <- twitter_20190919[[2]]
# mydata is object ready to analyze in RSiena. Nodes are the same as in keyf and seats. Contains twitter data at three timepounts. Three layers: fnet (who follows whom), atmnet (who amentions whom) and rnter (who retweets whom). Also contains timevariant information on nodes.
seats <- twitter_20190919[[3]]
#seats is dataset which contains coordinates of seats in House of Parliament in Netherlands




```


Densities
```{r}
# retrieve nominationdata from rsiena object
fnet <- mydata$depvars$fnet
atmnet <- mydata$depvars$atmnet
rtnet <- mydata$depvars$rtnet

# retrieve node-attributes from rsiena object
vrouw <- mydata$cCovars$vrouw
partij <- mydata$cCovars$partij
ethminz <- mydata$cCovars$ethminz
lft <- mydata$cCovars$lft

# de-mean-center node attributes
ethminz <- ethminz + attributes(ethminz)$mean
partij <- partij + attributes(partij)$mean
vrouw <- vrouw + attributes(vrouw)$mean
lft <- lft + attributes(lft)$mean

# construct matrices for similarity for each dimension (dyad characteristics)
vrouwm <- fhomomat(vrouw)
partijm <- fhomomat(partij)
ethminzm <- fhomomat(ethminz)

# just for fun, make dyad characteristic indicating whether both nodes are ethnic minorities
xmat <- matrix(ethminz, nrow = length(ethminz), ncol = length(ethminz))
xmatt <- t(xmat)
minoritym <- xmat == 1 & xmatt == 1

# for age max 5 year difference / for descriptives
xmat <- matrix(lft, nrow = length(lft), ncol = length(lft))
xmatt <- t(xmat)
lftm <- (abs(xmat - xmatt) < 6)

# calculate all possible similar dyads, not the focus of this exercise.  fndyads2(fnet[,,1], vrouwm)
# fndyads2(fnet[,,3], vrouwm) fndyads2(fnet[,,1], partijm) fndyads2(fnet[,,3], partijm)
# fndyads2(fnet[,,1], ethminzm) fndyads2(fnet[,,3], ethminzm)

# make a big object to store all results
desmat <- matrix(NA, nrow = 10, ncol = 9)

# lets start using our functions
desmat[1, 1] <- fdensity(fnet[, , 1])
desmat[1, 2] <- fdensity(fnet[, , 2])
desmat[1, 3] <- fdensity(fnet[, , 3])
desmat[2, 1] <- fdensityintra(fnet[, , 1], vrouwm)
desmat[2, 2] <- fdensityintra(fnet[, , 2], vrouwm)
desmat[2, 3] <- fdensityintra(fnet[, , 3], vrouwm)
desmat[3, 1] <- fdensityinter(fnet[, , 1], vrouwm)
desmat[3, 2] <- fdensityinter(fnet[, , 2], vrouwm)
desmat[3, 3] <- fdensityinter(fnet[, , 3], vrouwm)
desmat[4, 1] <- fdensityintra(fnet[, , 1], partijm)
desmat[4, 2] <- fdensityintra(fnet[, , 2], partijm)
desmat[4, 3] <- fdensityintra(fnet[, , 3], partijm)
desmat[5, 1] <- fdensityinter(fnet[, , 1], partijm)
desmat[5, 2] <- fdensityinter(fnet[, , 2], partijm)
desmat[5, 3] <- fdensityinter(fnet[, , 3], partijm)
desmat[6, 1] <- fdensityintra(fnet[, , 1], ethminzm)
desmat[6, 2] <- fdensityintra(fnet[, , 2], ethminzm)
desmat[6, 3] <- fdensityintra(fnet[, , 3], ethminzm)
desmat[7, 1] <- fdensityinter(fnet[, , 1], ethminzm)
desmat[7, 2] <- fdensityinter(fnet[, , 2], ethminzm)
desmat[7, 3] <- fdensityinter(fnet[, , 3], ethminzm)
desmat[8, 1] <- fdensityinter(fnet[, , 1], minoritym)
desmat[8, 2] <- fdensityinter(fnet[, , 2], minoritym)
desmat[8, 3] <- fdensityinter(fnet[, , 3], minoritym)
desmat[9, 1] <- fdensityintra(fnet[, , 1], lftm)
desmat[9, 2] <- fdensityintra(fnet[, , 2], lftm)
desmat[9, 3] <- fdensityintra(fnet[, , 3], lftm)
desmat[10, 1] <- fdensityinter(fnet[, , 1], lftm)
desmat[10, 2] <- fdensityinter(fnet[, , 2], lftm)
desmat[10, 3] <- fdensityinter(fnet[, , 3], lftm)

desmat[1, 1 + 3] <- fdensity(atmnet[, , 1])
desmat[1, 2 + 3] <- fdensity(atmnet[, , 2])
desmat[1, 3 + 3] <- fdensity(atmnet[, , 3])
desmat[2, 1 + 3] <- fdensityintra(atmnet[, , 1], vrouwm)
desmat[2, 2 + 3] <- fdensityintra(atmnet[, , 2], vrouwm)
desmat[2, 3 + 3] <- fdensityintra(atmnet[, , 3], vrouwm)
desmat[3, 1 + 3] <- fdensityinter(atmnet[, , 1], vrouwm)
desmat[3, 2 + 3] <- fdensityinter(atmnet[, , 2], vrouwm)
desmat[3, 3 + 3] <- fdensityinter(atmnet[, , 3], vrouwm)
desmat[4, 1 + 3] <- fdensityintra(atmnet[, , 1], partijm)
desmat[4, 2 + 3] <- fdensityintra(atmnet[, , 2], partijm)
desmat[4, 3 + 3] <- fdensityintra(atmnet[, , 3], partijm)
desmat[5, 1 + 3] <- fdensityinter(atmnet[, , 1], partijm)
desmat[5, 2 + 3] <- fdensityinter(atmnet[, , 2], partijm)
desmat[5, 3 + 3] <- fdensityinter(atmnet[, , 3], partijm)
desmat[6, 1 + 3] <- fdensityintra(atmnet[, , 1], ethminzm)
desmat[6, 2 + 3] <- fdensityintra(atmnet[, , 2], ethminzm)
desmat[6, 3 + 3] <- fdensityintra(atmnet[, , 3], ethminzm)
desmat[7, 1 + 3] <- fdensityinter(atmnet[, , 1], ethminzm)
desmat[7, 2 + 3] <- fdensityinter(atmnet[, , 2], ethminzm)
desmat[7, 3 + 3] <- fdensityinter(atmnet[, , 3], ethminzm)
desmat[8, 1 + 3] <- fdensityinter(atmnet[, , 1], minoritym)
desmat[8, 2 + 3] <- fdensityinter(atmnet[, , 2], minoritym)
desmat[8, 3 + 3] <- fdensityinter(atmnet[, , 3], minoritym)
desmat[9, 1 + 3] <- fdensityintra(atmnet[, , 1], lftm)
desmat[9, 2 + 3] <- fdensityintra(atmnet[, , 2], lftm)
desmat[9, 3 + 3] <- fdensityintra(atmnet[, , 3], lftm)
desmat[10, 1 + 3] <- fdensityinter(atmnet[, , 1], lftm)
desmat[10, 2 + 3] <- fdensityinter(atmnet[, , 2], lftm)
desmat[10, 3 + 3] <- fdensityinter(atmnet[, , 3], lftm)

desmat[1, 1 + 6] <- fdensity(rtnet[, , 1])
desmat[1, 2 + 6] <- fdensity(rtnet[, , 2])
desmat[1, 3 + 6] <- fdensity(rtnet[, , 3])
desmat[2, 1 + 6] <- fdensityintra(rtnet[, , 1], vrouwm)
desmat[2, 2 + 6] <- fdensityintra(rtnet[, , 2], vrouwm)
desmat[2, 3 + 6] <- fdensityintra(rtnet[, , 3], vrouwm)
desmat[3, 1 + 6] <- fdensityinter(rtnet[, , 1], vrouwm)
desmat[3, 2 + 6] <- fdensityinter(rtnet[, , 2], vrouwm)
desmat[3, 3 + 6] <- fdensityinter(rtnet[, , 3], vrouwm)
desmat[4, 1 + 6] <- fdensityintra(rtnet[, , 1], partijm)
desmat[4, 2 + 6] <- fdensityintra(rtnet[, , 2], partijm)
desmat[4, 3 + 6] <- fdensityintra(rtnet[, , 3], partijm)
desmat[5, 1 + 6] <- fdensityinter(rtnet[, , 1], partijm)
desmat[5, 2 + 6] <- fdensityinter(rtnet[, , 2], partijm)
desmat[5, 3 + 6] <- fdensityinter(rtnet[, , 3], partijm)
desmat[6, 1 + 6] <- fdensityintra(rtnet[, , 1], ethminzm)
desmat[6, 2 + 6] <- fdensityintra(rtnet[, , 2], ethminzm)
desmat[6, 3 + 6] <- fdensityintra(rtnet[, , 3], ethminzm)
desmat[7, 1 + 6] <- fdensityinter(rtnet[, , 1], ethminzm)
desmat[7, 2 + 6] <- fdensityinter(rtnet[, , 2], ethminzm)
desmat[7, 3 + 6] <- fdensityinter(rtnet[, , 3], ethminzm)
desmat[8, 1 + 6] <- fdensityinter(rtnet[, , 1], minoritym)
desmat[8, 2 + 6] <- fdensityinter(rtnet[, , 2], minoritym)
desmat[8, 3 + 6] <- fdensityinter(rtnet[, , 3], minoritym)
desmat[9, 1 + 6] <- fdensityintra(rtnet[, , 1], lftm)
desmat[9, 2 + 6] <- fdensityintra(rtnet[, , 2], lftm)
desmat[9, 3 + 6] <- fdensityintra(rtnet[, , 3], lftm)
desmat[10, 1 + 6] <- fdensityinter(rtnet[, , 1], lftm)
desmat[10, 2 + 6] <- fdensityinter(rtnet[, , 2], lftm)
desmat[10, 3 + 6] <- fdensityinter(rtnet[, , 3], lftm)

colnames(desmat) <- c("friends w1", "friends w2", "friends w3", "atmentions w1", "atmentions w2", "atmentions w3", 
    "retweets w1", "retweets w2", "retweets w3")
rownames(desmat) <- c("total", "same sex", "different sex", "same party", "different party", "same ethnicity", 
    "different ethnicity", "both minority", "same age (<6)", "different age (>5)")
desmat



# we observe a lot of homophily. Mainly big difference in density between and whithin political parties. Homophily is not that strong across social dimensions.
```


coleman homophily
```{r}
# Because size of different subgroups vary and number of out-degrees differs between MPs, whitin party densities might be higher when MPs randomly select partner/alter. Segregation will partly be structully induced by differences in relative groups sizes and activity on twitter. Coleman's homophily index: takes relative group sizes and differences into account. 0 -> observed number of within-group ties is the same as would be expected under random choice. 1 -> maximum segregation. -1 -> MPs maximally avoid within group relations.


colmat <- matrix(NA, nrow = 3, ncol = 9)

colmat[1, 1] <- fscolnet(fnet[, , 1], partij)
colmat[1, 2] <- fscolnet(fnet[, , 2], partij)
colmat[1, 3] <- fscolnet(fnet[, , 3], partij)
colmat[1, 4] <- fscolnet(atmnet[, , 1], partij)
colmat[1, 5] <- fscolnet(atmnet[, , 2], partij)
colmat[1, 6] <- fscolnet(atmnet[, , 3], partij)
colmat[1, 7] <- fscolnet(rtnet[, , 1], partij)
colmat[1, 8] <- fscolnet(rtnet[, , 2], partij)
colmat[1, 9] <- fscolnet(rtnet[, , 3], partij)

colmat[2, 1] <- fscolnet(fnet[, , 1], vrouw)
colmat[2, 2] <- fscolnet(fnet[, , 2], vrouw)
colmat[2, 3] <- fscolnet(fnet[, , 3], vrouw)
colmat[2, 4] <- fscolnet(atmnet[, , 1], vrouw)
colmat[2, 5] <- fscolnet(atmnet[, , 2], vrouw)
colmat[2, 6] <- fscolnet(atmnet[, , 3], vrouw)
colmat[2, 7] <- fscolnet(rtnet[, , 1], vrouw)
colmat[2, 8] <- fscolnet(rtnet[, , 2], vrouw)
colmat[2, 9] <- fscolnet(rtnet[, , 3], vrouw)

colmat[3, 1] <- fscolnet(fnet[, , 1], ethminz)
colmat[3, 2] <- fscolnet(fnet[, , 2], ethminz)
colmat[3, 3] <- fscolnet(fnet[, , 3], ethminz)
colmat[3, 4] <- fscolnet(atmnet[, , 1], ethminz)
colmat[3, 5] <- fscolnet(atmnet[, , 2], ethminz)
colmat[3, 6] <- fscolnet(atmnet[, , 3], ethminz)
colmat[3, 7] <- fscolnet(rtnet[, , 1], ethminz)
colmat[3, 8] <- fscolnet(rtnet[, , 2], ethminz)
colmat[3, 9] <- fscolnet(rtnet[, , 3], ethminz)

colnames(colmat) <- c("friends w1", "friends w2", "friends w3", "atmentions w1", "atmentions w2", "atmentions w3", 
    "retweets w1", "retweets w2", "retweets w3")
rownames(colmat) <- c("party", "sex", "ethnicity")
colmat
```


RSienna
```{r}
# defining myeff object
library(RSiena)
myeff <- getEffects(mydata)
myeff

myeff_m1 <- myeff
myeff_m1 <- includeEffects(myeff_m1, sameX, interaction1 = "partij", name = "rtnet")



# I used a seed so you will probably see the same results
myalgorithm <- sienaAlgorithmCreate(projname = "test", seed = 345654)



# to speed things up a bit, I am using more cores.
ansM1 <- siena07(myalgorithm, data = mydata, effects = myeff_m1, useCluster = TRUE, nbrNodes = 4, initC = TRUE, 
    batch = TRUE)
ansM1b <- siena07(myalgorithm, data = mydata, prevAns = ansM1, effects = myeff_m1, useCluster = TRUE, 
    nbrNodes = 4, initC = TRUE, batch = TRUE)
ansM1c <- siena07(myalgorithm, data = mydata, prevAns = ansM1b, effects = myeff_m1, useCluster = TRUE, 
    nbrNodes = 4, initC = TRUE, batch = TRUE)

save(ansM1, file = "ansM1a.RData")
save(ansM1b, file = "ansM1b.RData")
save(ansM1c, file = "ansM1c.RData")



load("ansM1a.RData")
load("ansM1b.RData")
load("ansM1c.RData")
ansM1

ansM1b

ansM1c


#To what extent do we observe segregation along party affiliation in the retweet network among Dutch MPs?

# Answer: the eval same partij is 1.8551. This means that we observe strong segregation along pary affiliation: people are more likely to have ties with people from the same party

```


RQ 1
```{r}
myeff_m2 <- myeff
myeff_m2 <- includeEffects(myeff_m2, sameX, interaction1 = "partij", name = "rtnet")


myeff_m2 <- myeff
myeff_m2 <- includeEffects(myeff_m2, sameX, interaction1 = "vrouw", name = "rtnet")


myeff_m2 <- myeff
myeff_m2 <- includeEffects(myeff_m2, sameX, interaction1 = "lft", name = "rtnet")

myeff_m2 <- myeff
myeff_m2 <- includeEffects(myeff_m2, X, interaction1 = "afstand", name = "rtnet")
```



```{r}

```

